Energy-based process for the detection of signals drowned in noise

Abstract

The energy-based process according to the invention for the detection of useful signals drowned in noise consists of starting from a frame of samples of a noisy signal grouped in successive frames, making a pre-classification by comparing the energies of successive samples of each frame with a determined optimum threshold and sorting samples which have a high probability of belonging to a "noise only" class into this class, and then for each of these samples detecting those that have a sufficiently high energy so that they have a high probability of belonging to a "noise+useful signal" class, this second class being defined using the first class as a reference.

I claim: 
     
       1. A process for detecting a transmitted useful signal drowned in noise, comprising the steps of: receiving a noisy signal;   partitioning a portion of the received noisy signal into L frames of N samples;   calculating energies of each of said L frames;   determining an optimum threshold, s;   preclassifying M of said L frames into a set Δ by using a predetermined set of ratios, m, α 1  and α 2  which define characteristic signal-to-noise ratios of the noisy signal;   calculating an average noise energy value, E 0 , from the frames in Δ as determined in the preclassifying step; and   detecting for each frame not in set Δ if a useful signal exists by using the average noise energy value, E 0 .   
     
     
       2. The process according to claim 1, wherein the step of preclassifying comprises the steps of: (a) determining a frame, T i0 , with the lowest energy, E(T i0 ), of said L frames;   (b) assigning frame T i0  to set Δ such that Δ={T i0  };   (c) selecting a current frame, T i , from frames T 1  . . . T L  which is not in Δ;   (d) determining if 1/s<E(T i )/E(T j )<s for each element, Tj, in set Δ;   (e) adding T i  to Δ if 1/s<E(T i )/E(T j )<s, as determined in step (d); and   (f) repeating steps (c) through (d) until all frames except T i0  have been selected.   
     
     
       3. The process according to claim 1, wherein the step of determining an optimum threshold, s, comprises: calculating the optimum threshold, s, using the maximum probability criterion when the correct decision probability is known.   
     
     
       4. The process according to claim 1, wherein the step of determining an optimum threshold, s, comprises: calculating the optimum threshold, s, using the Neyman-Pearson criterion when the correct decision probability is not known.   
     
     
       5. The process according to claim 1, wherein the step of detecting detects a useful frame if pf(X,m|α 1 ,M 1/2  α 2 )>(1-p)f(X,1|α 2 ,M 1/2  α 2 ) is true, wherein X=E(T i )/E 0 , p=the maximum probability criterion when the correct decision probability is known, ##EQU13## F is the distribution function of a Gaussian variable, P(x,m|α 1 ,α 2 )=Pr {X<x}, P(x,m|α 1 ,α 2 )=F h(x,y|α,.beta.)! and ##EQU14##   
     
     
       6. The process according to claim 1, wherein the step of detecting detects a useful frame if pf(X,m|α 1 ,M 1/2  α 2 )>(1-p)f(X,1|α 2 ,M 1/2  α 2 ) is true, wherein X=E(T i )/E 0  where p is calculating by using the Neyman-Pearson criterion when the correct decision probability is not known, ##EQU15## F is the distribution function of a Gaussian variable, P(x, m|α 1 ,α 2 )=Pr {X<x}, P(x, m|α 1 ,α 2 )=F h(x,y|α,β)! and ##EQU16##   
     
     
       7. The process according to claim 1, wherein the step of detecting detects a useful frame if E(T i )/E 0  >s is true when using threshold detection.   
     
     
       8. A process for detecting a transmitted useful signal drowned in noise, comprising the steps of: receiving a noisy signal;   partitioning a portion of the received noisy signal into L frames of N samples;   calculating energies of each of said L frames;   determining an optimum threshold, s;   preclassifying M of said L frames into a set Δ by using a predetermined set of ratios, m, α 1  and α 2  which define characteristic signal-to-noise ratios of the noisy signal;   calculating an average noise energy value, E 0 , from the frames in Δ as determined in the preclassifying step;   whitening each of said L frames not in α; and   detecting for each frame not in set Δ if a useful signal exists by using the average noise energy value, E 0 .   
     
     
       9. The process according to claim 8, wherein the step of preclassifying comprises the steps of: (a) determining a frame, T i0 , with the lowest energy, E(T i0 ), of said L frames;   (b) assigning frame T i0  to set Δ such that Δ={T i0  };   (c) selecting a current frame, T i , from frames T 1  . . . T L  which is not in Δ;   (d) determining if 1/s<E(T i )/E(T j )<s for each element, Tj, in set Δ;   (e) adding T i  to Δ if 1/s<E(T i )/E(T j )<s, as determined in step (d); and   (f) repeating steps (c) through (d) until all frames except T i0  have been selected.   
     
     
       10. The process according to claim 8, wherein the step of determining an optimum threshold, s, comprises: calculating the optimum threshold, s, using the maximum probability criterion when the correct decision probability is known.   
     
     
       11. The process according to claim 8, wherein the step of determining an optimum threshold, s, comprises: calculating the optimum threshold, s, using the Neyman-Pearson criterion when the correct decision probability is not known.   
     
     
       12. The process according to claim 8, wherein the step of detecting detects a useful frame if pf(X,m|α 1 ,M 1/2  α 2 )>(1-p)f(X,1|α 2 ,M 1/2  α 2 ) is true, wherein X=E(T i )/E 0 , p=the maximum probability criterion when the correct decision probability is known, ##EQU17## F is the distribution function of a Gaussian variable, P(x,m|α 1 ,α 2 )=Pr {X<x}, P(x,m|α 1 ,α 2 )=F h(x,y|α,.beta.)! and ##EQU18##   
     
     
       13. The process according to claim 8, wherein the step of detecting detects a useful frame if pf(X,m|α 1 ,M 1/2  α 2 )>(1-p)f(X,1|α 2 ,M 1/2  α 2 ) is true, wherein X=E(T i )/E 0  where p is calculating by using the Neyman-Pearson criterion when the correct decision probability is not known, ##EQU19## F is the distribution function of a Gaussian variable, P(x, m|α 1 ,α 2 )=Pr {X<x}, P(x, m|α 1 ,α 2 )=F h(x,y|α,β)! and ##EQU20##   
     
     
       14. The process according to claim 8, wherein the step of detecting detects a useful frame if E(T i )/E 0  >s is true when using threshold detection.   
     
     
       15. A process for detecting a transmitted useful signal drowned in noise, comprising the steps of: receiving a noisy signal;   partitioning a portion of the received noisy signal into L frames of N samples;   calculating energies of each of said L frames;   determining an optimum threshold, s;   preclassifying M of said L frames into a set Δ by using a predetermined set of ratios, m, α 1  and α 2  which define characteristic signal-to-noise ratios of the noisy signal;   calculating an average noise energy value, E 0 , from the frames in Δ as determined in the preclassifying step;   filtering each of said L frames not in Δ; and   detecting for each frame not in set Δ if a useful signal exists by using the average noise energy value, E 0 .   
     
     
       16. The process according to claim 15, wherein the step of preclassifying comprises the steps of: (a) determining a frame, T i0 , with the lowest energy, E(T i0 ), of said L frames;   (b) assigning frame T i0  to set Δ such that Δ={T i0  };   (c) selecting a current frame, T i , from frames T 1  . . . T L  which is not in Δ;   (d) determining if 1/s<E(T i )/E(T j )<s for each element, Tj, in set Δ;   (e) adding T i  to Δ if 1/s<E(T i )/E(T j )<s, as determined in step (d); and   (f) repeating steps (c) through (d) until all frames except T i0  have been selected.   
     
     
       17. The process according to claim 15, wherein the step of determining an optimum threshold, s, comprises: calculating the optimum threshold, s, using the maximum probability criterion when the correct decision probability is known.   
     
     
       18. The process according to claim 15, wherein the step of determining an optimum threshold, s, comprises: calculating the optimum threshold, s, using the Neyman-Pearson criterion when the correct decision probability is not known.   
     
     
       19. The process according to claim 15, wherein the step of detecting detects a useful frame if pf(X,m|α 1 ,M 1/2  α 2 )>(1-p)f(X,1|α 2 ,M 1/2  α 2 ) is true, wherein X=E(T i )/E 0 , p=the maximum probability criterion when the correct decision probability is known, ##EQU21## F is the distribution function of a Gaussian variable, P(x,m|α 1 ,α 2 )=Pr {X<x}, P(x,m|α 1 ,α 2 )=F h(x,y|α,.beta.)! and ##EQU22##   
     
     
       20. The process according to claim 15, wherein the step of detecting detects a useful frame if pf(X,m|α 1 ,M 1/2  α 2 )>(1-p)f(X,1|α 2 ,M 1/2  α 2 ) is true, wherein X=E(T i )/E 0  where p is calculating by using the Neyman-Pearson criterion when the correct decision probability is not known, ##EQU23## F is the distribution function of a Gaussian variable, P(x, m|α 1 ,α 2 )=Pr {X<x}, P(x, m|α 1 ,α 2 )=F h(x,y|α,β)! and ##EQU24##   
     
     
       21. The process according to claim 15, wherein the step of detecting detects a useful frame if E(T i )/E 0  >s is true when using threshold detection.